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Python Machine Learning for BeginnersMachine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that's right. Based on a significant amount of data and evidence, it's obvious that ML and AI are here to stay.Consider any industry today. The practical applications of ML are really driving business results. Whether it's healthcare, e-commerce, government, transportation, social media sites, financial services, manufacturing, oil and gas, marketing and salesYou name it. The list goes on. There's no doubt that ML is going to play a decisive role in every domain in the future.But what does a Machine Learning professional do?A Machine Learning specialist develops intelligent algorithms that learn from data and also adapt to the data quickly. Then, these high-end algorithms make accurate predictions.Python Machine Learning for Beginners presents you with a hands-on approach to learn ML fast.How Is This Book Different?AI Publishing strongly believes in learning by doing methodology. With this in mind, we have crafted this book with care. You will find that the emphasis on the theoretical aspects of machine learning is equal to the emphasis on the practical aspects of the subject matter.You'll learn about data analysis and visualization in great detail in the first half of the book. Then, in the second half, you'll learn about machine learning and statistical models for data science.Each chapter presents you with the theoretical framework behind the different data science and machine learning techniques, and practical examples illustrate the working of these techniques.When you buy this book, your learning journey becomes so much easier. The reason is you get instant access to all the related learning material presented with this book-references, PDFs, Python codes, and exercises-on the publisher's website. All this material is available to you at no extra cost. You can download the ML datasets used in this book at runtime, or you can access them via the Resources/Datasets folder.You'll also find the short course on Python programming in the second chapter immensely useful, especially if you are new to Python. Since this book gives you access to all the Python codes and datasets, you only need access to a computer with the internet to get started. The topics covered include:Introduction and Environment SetupPython Crash CoursePython NumPy Library for Data AnalysisIntroduction to Pandas Library for Data AnalysisData Visualization via Matplotlib, Seaborn, and Pandas LibrariesSolving Regression Problems in ML Using Sklearn LibrarySolving Classification Problems in ML Using Sklearn LibraryData Clustering with ML Using Sklearn LibraryDeep Learning with Python TensorFlow 2.0Dimensionality Reduction with PCA and LDA Using SklearnClick the BUY NOW button to start your Machine Learning journey.



Publié par
Date de parution 23 octobre 2020
Nombre de lectures 37
EAN13 9781956591026
Langue English
Poids de l'ouvrage 4 Mo

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Table of Contents
Book Approach
Who Is This Book For?
How to Use This Book?
About the Author
Chapter 1: Introduction and Environment Set Up
1.1. Difference between Data Science and Machine Learning
1.2. Steps in Learning Data Science and Machine Learning
1.3. Environment Setup
1.3.1. Windows Setup
1.3.2. Mac Setup
1.3.3. Linux Setup
1.3.4. Using Google Colab Cloud Environment
Chapter 2: Python Crash Course
2.1. Writing Your First Program
2.2. Python Variables and Data Types
2.3. Python Operators
2.4. Conditional Statements
2.5. Iteration Statements
2.6. Functions
2.7. Objects and Classes
2.8. Data Science and Machine Learning Libraries
2.8.1 NumPy
2.8.2. Matplotlib
2.8.3. Seaborn
2.8.4. Pandas
2.8.5. Scikit Learn
2.8.6. TensorFlow
2.8.7. Keras
Exercise 2.1
Exercise 2.2
Chapter 3: Python NumPy Library for Data Analysis
3.1. Advantages of NumPy Library
3.2. Creating NumPy Arrays
3.2.1 Using Array Methods
3.2.2. Using Arrange Method
3.2.3. Using Ones Method
3.2.4. Using Zeros Method
3.2.5. Using Eyes Method
3.2.6. Using Random Method
3.3. Reshaping NumPy Arrays
3.4. Array Indexing and Slicing
3.5. NumPy for Arithmetic Operations
3.5.1. Finding Square Roots
3.5.2. Finding Logs
3.5.3. Finding Exponents
3.5.4. Finding Sine and Cosine
3.6. NumPy for Linear Algebra Operations
3.6.1. Finding Matrix Dot Product
3.6.2. Element-wise Matrix Multiplication
3.6.3. Finding Matrix Inverse
3.6.4. Finding Matrix Determinant
3.6.5. Finding Matrix Trace
Exercise 3.1
Exercise 3.2
Chapter 4: Introduction to Pandas Library for Data Analysis
4.1. Introduction
4.2. Reading Data into Pandas Dataframe
4.3. Filtering Rows
4.4. Filtering Columns
4.5. Concatenating Dataframes
4.6. Sorting Dataframes
4.7. Apply Function
4.8. Pivot & Crosstab
4.9. Arithmetic Operations with Where
Exercise 4.1
Exercise 4.2
Chapter 5: Data Visualization via Matplotlib, Seaborn, and Pandas Libraries
5.1. What is Data Visualization?
5.2. Data Visualization via Matplotlib
5.2.1. Line Plots
5.2.2. Titles, Labels, and Legends
5.2.3. Plotting Using CSV and TSV files
5.2.4. Scatter Plots
5.2.5. Bar Plots
5.2.6. Histograms
5.2.7. Pie Charts
5.3. Data Visualization via Seaborn
5.3.1. The Dist Plot
5.3.2 The Joint Plot
5.3.3. The Pair Plot
5.3.4. The Bar Plot
5.3.5. The Count Plot
5.3.6. The Box Plot
5.3.7. The Violin Plot
5.4. Data Visualization via Pandas
5.4.1. Loading Datasets with Pandas
5.4.2. Plotting Histograms with Pandas
5.4.3. Pandas Line Plots
5.4.4. Pandas Scatter Plots
5.4.5. Pandas Bar Plots
5.4.6. Pandas Box Plots
Exercise 5.1
Exercise 5.2
Chapter 6: Solving Regression Problems in Machine Learning Using Sklearn Library
6.1. Preparing Data for Regression Problems
6.1.1. Dividing Data into Features and Labels
6.1.2. Converting Categorical Data to Numbers
6.1.3. Divide Data into Training and Test Sets
6.1.4. Data Scaling/Normalization
6.2. Linear Regression
6.3. KNN Regression
6.4. Random Forest Regression
6.5. Support Vector Regression
6.6. K Fold Cross-Validation
6.7. Making Prediction on a Single Record
Exercise 6.1
Exercise 6.2
Chapter 7: Solving Classification Problems in Machine Learning Using Sklearn Library
7.1. Preparing Data for Classification Problems
7.1.1. Dividing Data into Features and Labels
7.1.2. Converting Categorical Data to Numbers
7.1.3. Divide Data into Training and Test Sets
7.1.4. Data Scaling/Normalization
7.2. Logistic Regression
7.3. KNN Classifier
7.4. Random Forest Classifier
7.5. Support Vector Classification
7.6. K-Fold Cross-Validation
7.7. Predicting a Single Value
Exercise 7.1
Exercise 7.2
Chapter 8: Data Clustering with Machine Learning Using Sklearn Library
8.1. K Means Clustering
8.1.1. Clustering Dummy Data with Sklearn
8.1.2. Clustering Iris Dataset
8.2. Hierarchical Clustering
8.2.1. Clustering Dummy Data
8.2.2. Clustering the Iris Dataset
Exercise 8.1
Exercise 8.2
Chapter 9: Deep Learning with Python TensorFlow 2.0
9.1. Densely Connected Neural Network
9.1.1. Feed Forward
9.1.2. Backpropagation
9.1.3. Implementing a Densely Connected Neural Network
Importing Required Libraries
Importing the Dataset
Dividing Data into Training and Test Sets
Creating a Neural Network
Evaluating the Neural Network Performance
9.2. Recurrent Neural Networks (RNN)
9.2.1. What Is an RNN and LSTM?
What Is an RNN?
Problems with RNN
What Is an LSTM?
9.3. Predicting Future Stock Prices via LSTM in Keras
9.3.1. Training the Stock Prediction Model
9.3.2. Testing the Stock Prediction Model
9.4. Convolutional Neural Network
9.4.1. Image Classification with CNN
9.4.2. Implementing CNN with TensorFlow Keras
Exercise 9.1
Exercise 9.2
Chapter 10: Dimensionality Reduction with PCA and LDA Using Sklearn
10.1. Principal Component Analysis
10.2. Linear Discriminant Analysis
Exercise 10.1
Exercise 10.2
From the Same Publisher
Exercises Solutions
Exercise 2.1
Exercise 2.2
Exercise 3.1
Exercise 3.2
Exercise 4.1
Exercise 4.2
Exercise 5.1
Exercise 5.2
Exercise 6.1
Exercise 6.2
Exercise 7.1
Exercise 7.2
Exercise 8.1
Exercise 8.2
Exercise 9.1
Exercise 9.2
Exercise 10.1
Exercise 10.2
Thank you for your decision on purchasing this book. I can assure you that you will not regret your decision. The saying data is the new oil is no longer a mere cliche. Data is actually powering the industries of today. Organizations and companies need to improve their growth, which depends on correct decision making. Accurate decision making requires facts and figures and statistical analysis of data. Data science does exactly that. With data and machine learning, you can extract and visualize data in detail and create statistical models, which, in turn, help you in decision making. In th

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